Digital imaging techniques in medicine were implemented in the 1970's with the first clinical use and acceptance of the Computed Tomography or CT scanner. Later, extensive use of x-ray imaging (CT) and the advent of the digital computer and new imaging modalities like ultrasound and magnetic resonance imaging (MRI) have combined to create an explosion of diagnostic imaging techniques in the past three decades.
There are benefit to using digital medical imaging technology in health care. For example, angiographic procedures for looking at the blood vessels in the brain, kidneys, arms and legs, and heart all have benefited from the adaptation of digital medical imaging and image processing technologies.
With digital images, computerized multi-dimensional (e.g., spatial and temporal) image analysis becomes possible. Multi-dimensional image analysis can be used in applications such as automatic quantification of changes (anatomical or functional) in serial image volume scans of body parts, foreign objects localization, consistent diagnostic rendering, and the like.
Also, different medical imaging modalities produce images providing different view of human body function and anatomy that have the potential of enhancing diagnostic accuracy dramatically with the help of the right medical image processing software and visualization tools. For example, X-ray computed tomography (CT) and magnetic resonance imaging (MRI) demonstrate brain anatomy but provide little functional information. Positron emission tomography (PET) and single photon emission computed tomography (SPECT) scans display aspects of brain function and allow metabolic measurements but poorly delineate anatomy. Furthermore, CT and MRI images describe complementary morphologic features. For example, bone and calcifications are best seen on CT images, while soft-tissue structures are better differentiated by MRI. Modalities such as MRI and CT usually provide a stack of images for certain body parts.
It is known that the information gained from different dimensions (spatial and temporal) or modalities is often of a difference or complementary nature. Within the current clinical setting, this difference or complementary image information is a component of a large number of applications in clinical diagnostics settings, and also in the area of planning and evaluation of surgical and radiotherapeutical procedures.
In order to effectively use the difference or complementary information, image features from different dimensions or different modalities had to be superimposed to each other by physicians using a visual alignment system. Unfortunately, such a coordination of multiple images with respect to each other is extremely difficult and even highly trained medical personnel, such as experienced radiologists, have difficulty in consistently and properly interpreting a series of medical images so that a treatment regime can be instituted which best fits the patient's current medical condition.
Another problem encountered by medical personnel today is the large amount of data and numerous images that are obtained from current medical imaging devices. The number of images collected in a standard scan can be in excess of 100 and frequently numbers in the many hundreds. In order for medical personnel to properly review each image takes a great deal of time and, with the many images that current medical technology provides, a great amount of time is required to thoroughly examine all the data.
Accordingly, there exists a need for an efficient approach that uses image processing/computer vision techniques to automatically detect/diagnose diseases.
U.S. Patent Application No. 2003/0095147 (Daw), incorporated herein by reference, relates to a computerized method of medical image processing and visualization. In Daw's method, a memory is provided for storing a plurality of data sets, each data set corresponding to an image of a location within a medical body of interest. Each image contains a number of features which correspond to data points that have been collected when creating an image of the medical body. The data points thus correspond to a measured parameter within the medical body. A visual display is provided of the image having a varying color scale for different regions of interest within the body. Medical personnel are able to select various regions of interest within the image for which further study is desired. In addition, within the region of interest the medical personnel may select a particular feature representing data corresponding to medical information within the body for which further study is desired and have the computer perform an analysis to compare to or locate other tissue of the same type elsewhere in the data sets. When such data analysis are performed on the images, analysis indicators are provided in the upper left hand corner of the display providing an easy to view indication of the results and status of any computer analysis being performed or that has been performed on the data. However, Daw's system fails to provide a function of automatically detect and differentiate image areas corresponding to materials (tissues) being imaged that have different time response to contrast enhancing agent. Such function is particularly useful in diagnosing malignant and benign breast tumors using MRI contrast enhanced images.
It is known that malignant breast tumors begin to grow their own blood supply network once they reach a certain size; this is the way the cancer can continue to grow. In a breast MRI scan, a contrast agent injected into the bloodstream can provide information about blood supply to the breast tissues; the agent “lights up” a tumor by highlighting its blood vessel network. Usually, several scans are taken: one before the contrast agent is injected and at least one after. The pre-contrast and post-contrast images are compared and areas of difference are highlighted. It should be recognized that if the patient moves even slightly between the two scans, the shape or size of the image may be distorted—a big loss of information.
An contrast agent for MRI is Gadolinium or gadodiamide, and provides contrast between normal tissue and abnormal tissue in the brain and body.
Gadolinium looks clear like water and is non-radioactive. After it is injected into a vein, Gadolinium accumulates in the abnormal tissue that may be affecting the body or head. Gadolinium causes these abnormal areas to become bright (enhanced) on the MRI. This makes it easy to see. Gadolinium is then cleared from the body by the kidneys. Gadolinium allows the MRI to define abnormal tissue with greater clarity. Tumors enhance after Gadolinium is given. The exact size of the tumor and location is important in treatment planning and follow up. Gadolinium is also helpful in finding small tumors by making them bright and easy to see.
Dynamic contrast enhanced MRI is used for breast cancer imaging; in particular for those situation that have an inconclusive diagnosis based on x-ray mammography. The MRI study involves intravenous injection of a contrast agent (typically gadopentetate dimeglumine) immediately prior to acquiring a set of T1-weighted MR volumes with a temporal resolution of around a minute. The presence of contrast agent within an imaging voxel results in an increased signal that can be observed over the time course of the experiment.
Study of these signal-time curves enables identification of different tissue types due to their differential contrast uptake properties as illustrated in FIG. 1. Typically, cancerous tissue shows a high and fast uptake due to a proliferation of “leaky” angiogenic microvessels, while normal and fatty tissues show little uptake. The uptake (dynamic) curves have often been fitted using a pharmacokinetic model to give a physiologically relevant parameterisation of the curve (refer to P. S. Tofts, B. Berkowitz, M. Schnall, “Quantitative analysis of dynamic Gd-DTPA enhancement in breast tumours using a permeability model”, Magn Reson Med 33, pp 564-568, 1995).
U.S. Pat. No. 6,353,803 (Degani, Hadassa), incorporated herein by reference, is directed to an apparatus and method for monitoring a system in which a fluid flows and which is characterized by a change in the system with time in space. A preselected place in the system is monitored to collect data at two or more time points correlated to a system event. The data is indicative of a system parameter that varies with time as a function of at least two variables related to system wash-in and wash-out behavior.
Study of these curves/parameters has been used clinically to identify and characterize tumors into malignant or benign classes, although the success has been variable with generally good sensitivity but often very poor specificity (refer to S. C. Rankin “MRI of the breast”, Br. J. Radiol 73, pp 806-818, 2000).
While such systems may have achieved certain degrees of success in their particular applications, there is a need for an improved digital image processing method for medical image analysis that overcomes the problems set forth above and addresses the utilitarian needs set forth above.
The present invention provides a method for automatically detecting and differentiating abnormal tissues in contrast enhanced MRI images.